from typing import List, Tuple

import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from autorag.nodes.passagereranker.base import passage_reranker_node
from autorag.utils.util import make_batch, sort_by_scores, flatten_apply, select_top_k


@passage_reranker_node
def koreranker(
	queries: List[str],
	contents_list: List[List[str]],
	scores_list: List[List[float]],
	ids_list: List[List[str]],
	top_k: int,
	batch: int = 64,
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
	"""
	Rerank a list of contents based on their relevance to a query using ko-reranker.
	ko-reranker is a reranker based on korean (https://huggingface.co/Dongjin-kr/ko-reranker).

	:param queries: The list of queries to use for reranking
	:param contents_list: The list of lists of contents to rerank
	:param scores_list: The list of lists of scores retrieved from the initial ranking
	:param ids_list: The list of lists of ids retrieved from the initial ranking
	:param top_k: The number of passages to be retrieved
	:param batch: The number of queries to be processed in a batch
	    Default is 64.
	:return: tuple of lists containing the reranked contents, ids, and scores
	"""
	model_path = "Dongjin-kr/ko-reranker"
	tokenizer = AutoTokenizer.from_pretrained(model_path)
	model = AutoModelForSequenceClassification.from_pretrained(model_path)
	model.eval()

	# Determine the device to run the model on (GPU if available, otherwise CPU)
	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	model.to(device)

	nested_list = [
		list(map(lambda x: [query, x], content_list))
		for query, content_list in zip(queries, contents_list)
	]
	scores_nps = flatten_apply(
		koreranker_run_model,
		nested_list,
		model=model,
		batch_size=batch,
		tokenizer=tokenizer,
		device=device,
	)

	rerank_scores = list(
		map(lambda scores: exp_normalize(np.array(scores)).astype(float), scores_nps)
	)

	df = pd.DataFrame(
		{
			"contents": contents_list,
			"ids": ids_list,
			"scores": rerank_scores,
		}
	)
	df[["contents", "ids", "scores"]] = df.apply(
		sort_by_scores, axis=1, result_type="expand"
	)
	results = select_top_k(df, ["contents", "ids", "scores"], top_k)

	del model
	if torch.cuda.is_available():
		torch.cuda.empty_cache()

	return (
		results["contents"].tolist(),
		results["ids"].tolist(),
		results["scores"].tolist(),
	)


def koreranker_run_model(input_texts, model, tokenizer, device, batch_size: int):
	batch_input_texts = make_batch(input_texts, batch_size)
	results = []
	for batch_texts in tqdm(batch_input_texts):
		inputs = tokenizer(
			batch_texts,
			padding=True,
			truncation=True,
			return_tensors="pt",
			max_length=512,
		)
		inputs = inputs.to(device)
		with torch.no_grad():
			scores = (
				model(**inputs, return_dict=True)
				.logits.view(
					-1,
				)
				.float()
			)
			scores_np = scores.cpu().numpy()
			results.extend(scores_np)
	return results


def exp_normalize(x):
	b = x.max()
	y = np.exp(x - b)
	return y / y.sum()
